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Digital Object Identifier (DOI) : 10.14569/SpecialIssue.2011.010321
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Artificial Intelligence, 2011.
Abstract: The increase in number of cancer is detected throughout the world. This leads to the requirement of developing a new technique which can detect the occurrence the cancer. This will help in better diagnosis in order to reduce the cancer patients. This paper aim at finding the smallest set of genes that can ensure highly accurate classification of cancer from micro array data by using supervised machine learning algorithms. The significance of finding the minimum subset is three fold: a) The computational burden and noise arising from irrelevant genes are much reduced; b) the cost for cancer testing is reduced significantly as it simplifies the gene expression tests to include only a very small number of genes rather than thousands of genes; c) it calls for more investigation into the probable biological relationship between these small numbers of genes and cancer development and treatment. The proposed method involves two steps. In the first step, some important genes are chosen with the help of Analysis of Variance (ANOVA) ranking scheme. In the second step, the classification capability is tested for all simple combinations of those important genes using a better classifier. The proposed method uses Fast Adaptive Neuro-Fuzzy Inference System (FANFIS) as a classification model. This classification model uses Modified Levenberg-Marquardt algorithm for learning phase. The experimental results suggest that the proposed method results in better accuracy and also it takes lesser time for classification when compared to the conventional techniques.
K Anandakumar and M.Punithavalli, “Efficient Cancer Classification using Fast Adaptive Neuro-Fuzzy Inference System (FANFIS) based on Statistical Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Artificial Intelligence, 2011. http://dx.doi.org/10.14569/SpecialIssue.2011.010321